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SAMCNet for Spatial-configuration-based Classification: A Summary of Results
Majid Farhadloo, Carl Molnar, Gaoxiang Luo +6
The goal of spatial-configuration-based classification is to build a classifier to distinguish two classes (e.g., responder, non-responder) based on the spatial arrangements (e.g.,…
Towards Comparative Physical Interpretation of Spatial Variability Aware Neural Networks: A Summary of Results
Jayant Gupta, Carl Molnar, Gaoxiang Luo +2
Given Spatial Variability Aware Neural Networks (SVANNs), the goal is to investigate mathematical (or computational) models for comparative physical interpretation towards their tr…
Vehicle Emissions Prediction with Physics-Aware AI Models: Preliminary Results
Harish Panneer Selvam, Yan Li, Pengyue Wang +2
Given an on-board diagnostics (OBD) dataset and a physics-based emissions prediction model, this paper aims to develop an accurate and computational-efficient AI (Artificial Intell…
Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey
Yiqun Xie, Shashi Shekhar, Yan Li
Mapping of spatial hotspots, i.e., regions with significantly higher rates of generating cases of certain events (e.g., disease or crime cases), is an important task in diverse soc…